Similar to YOLO[1]}, the confidence of lanes is a binary branch. Each pixel represents the confidence of the cell containing lanes. We interpolate the lanes in the visible area of the training data. If there is a lane through the cell, the confidence scores should be one, otherwise the confidence scores should be zero. The confidence loss can be expressed by Binary Cross Entropy loss.
\(L_{conf}^{3d} = \sum \limits _i^{s1 \times s2} {({{\hat{p}}_i}\log {p_i} + (1 - {{\hat{p}}_i})\log (1 - {p_i}))}\) 
